Conventional methods for computing metrics of image distortion rely on the availability of a high-quality version of a given image with low or no distortion for reference. These metrics can be computed from differences in pixel values, such as mean-squared error (MSE) or peak signal-to-noise ratio (PSRN), or in simulations of the visual conspicuity of those differences, such as from a just-noticeable difference (JND) output of a visual discrimination model (VDM).
Simple pixel-difference metrics, such as PSNR, are often less sensitive to distortions due to blur than to noise, and can underestimate the impact of blur on subjective assessments of image quality. JND-based visual metrics have been found to correlate better with observer ratings of image degradation due to both blur and noise in cases where both types of distortions can be related to an “ideal” reference image without significant distortion.
Unfortunately, prior methods have not discriminated the effects of blur and noise on JND metrics. This capability would be desirable when the imaging methods and parameters to be evaluated produce fundamental tradeoffs between blur and noise, such as in single-shot fast spin echo (HASTE) image reconstruction. In addition, high-quality reference images are often not available for evaluating levels of image distortion. In these cases, an alternate method is desired.